FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing

• URL: http://arxiv.org/abs/2508.06256v1
• Date: Fri, 08 Aug 2025 12:21:58 GMT
• Title: FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing
• Authors: Barış Büyüktaş, Jonas Klotz, Begüm Demir,
• Abstract summary: Federated learning is a suitable learning paradigm for remote sensing (RS) image classification tasks.<n>A key challenge in applying FL to RS tasks is the communication overhead caused by the frequent exchange of large model updates between clients and the central server.<n>We propose a novel strategy (denoted as FedX) that uses explanation-guided pruning to reduce communication overhead.
• Score: 2.725507329935916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients), where each client stores data locally and only shares model updates with a central server. This makes FL a suitable learning paradigm for remote sensing (RS) image classification tasks, where data centralization may be restricted due to legal and privacy constraints. However, a key challenge in applying FL to RS tasks is the communication overhead caused by the frequent exchange of large model updates between clients and the central server. To address this issue, in this paper we propose a novel strategy (denoted as FedX) that uses explanation-guided pruning to reduce communication overhead by minimizing the size of the transmitted models without compromising performance. FedX leverages backpropagation-based explanation methods to estimate the task-specific importance of model components and prunes the least relevant ones at the central server. The resulting sparse global model is then sent to clients, substantially reducing communication overhead. We evaluate FedX on multi-label scene classification using the BigEarthNet-S2 dataset and single-label scene classification using the EuroSAT dataset. Experimental results show the success of FedX in significantly reducing the number of shared model parameters while enhancing the generalization capability of the global model, compared to both unpruned model and state-of-the-art pruning methods. The code of FedX will be available at https://git.tu-berlin.de/rsim/FedX.

Related papers

• Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models [6.138533689454442]
  Federated Learning enables decentralized clients to collaboratively train a shared model by exchanging model parameters instead of transmitting raw data.<n>We propose FedOL to construct a larger and more comprehensive server model in one-shot settings.<n>This reduces communication overhead by transmitting compact predictions instead of full model weights.
  arXiv  Detail & Related papers  (2025-08-19T08:35:25Z)
• Federated Split Learning with Improved Communication and Storage Efficiency [9.277834710955766]
  Federated learning (FL) is one of the popular distributed machine learning (ML) solutions but incurs significant communication and computation costs at edge devices.<n>This paper proposes a novel communication and storage efficient federated split learning method, CSE-FSL, which utilizes an auxiliary network to locally update the clients while keeping a single model at the server.
  arXiv  Detail & Related papers  (2025-07-21T17:21:16Z)
• Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classification [2.725507329935916]
  Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a global model.<n>Due to the large volume of model updates required to be transmitted between clients and the central server, most FL systems are associated with high transfer costs.<n>We introduce an explanation-guided pruning strategy for communication-efficient FL in the context of RS image classification.
  arXiv  Detail & Related papers  (2025-01-20T13:59:41Z)
• FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients [50.13097183691517]
  In real-world federated scenarios, there often exist a multitude of heterogeneous clients with varying computation and communication resources. We propose a novel federated tuning algorithm, FedRA. In each communication round, FedRA randomly generates an allocation matrix. It reorganizes a small number of layers from the original model based on the allocation matrix and fine-tunes using adapters.
  arXiv  Detail & Related papers  (2023-11-19T04:43:16Z)
• FedCode: Communication-Efficient Federated Learning via Transferring Codebooks [3.004066195320147]
  Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized local data. Existing approaches rely on model compression techniques, such as pruning and weight clustering to tackle this. We propose FedCode where clients transmit only codebooks, i.e., the cluster centers of updated model weight values.
  arXiv  Detail & Related papers  (2023-11-15T12:06:32Z)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)
• FedAvg with Fine Tuning: Local Updates Lead to Representation Learning [54.65133770989836]
  Federated Averaging (FedAvg) algorithm consists of alternating between a few local gradient updates at client nodes, followed by a model averaging update at the server. We show that the reason behind generalizability of the FedAvg's output is its power in learning the common data representation among the clients' tasks. We also provide empirical evidence demonstrating FedAvg's representation learning ability in federated image classification with heterogeneous data.
  arXiv  Detail & Related papers  (2022-05-27T00:55:24Z)
• An Expectation-Maximization Perspective on Federated Learning [75.67515842938299]
  Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated federated learning process as a hierarchical latent variable model where the server provides the parameters of a prior distribution over the client-specific model parameters. We show that with simple Gaussian priors and a hard version of the well known Expectation-Maximization (EM) algorithm, learning in such a model corresponds to FedAvg, the most popular algorithm for the federated learning setting.
  arXiv  Detail & Related papers  (2021-11-19T12:58:59Z)
• Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching [28.990067638230254]
  Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge. We propose a novel algorithm, Comdirectional, which allows clients to train large networks using representations of the global neural network.
  arXiv  Detail & Related papers  (2021-09-17T04:48:42Z)
• Federated Multi-Target Domain Adaptation [99.93375364579484]
  Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. We consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server. We propose an effective DualAdapt method to address the new challenges.
  arXiv  Detail & Related papers  (2021-08-17T17:53:05Z)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.